Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "168" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 36 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 34 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2459851 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 260.810769 | 259.845248 | inf | inf | 13787.732752 | 13787.704735 | 14631.146631 | 14634.690907 | nan | nan | nan | 0.000000 | 0.000000 |
| 2459850 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.58% | 100.00% | 0.00% | 1.478382 | 2.094663 | 0.205210 | -0.225372 | 0.501435 | 0.268022 | -0.094797 | 0.041398 | 0.7489 | 0.7657 | 0.3539 | 3.375103 | 2.817905 |
| 2459849 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 1.767493 | 2.060712 | 0.004101 | 1.662762 | 0.563941 | 0.305327 | 0.643569 | -0.146320 | 0.7455 | 0.7569 | 0.3632 | 4.063072 | 3.529827 |
| 2459848 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 2.061295 | 2.517948 | 1.328715 | 6.952587 | 1.482289 | 0.324796 | 0.477724 | -0.304011 | 0.7319 | 0.7639 | 0.3831 | 3.754155 | 3.178166 |
| 2459847 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 1.989204 | 2.459872 | 0.923457 | 6.296933 | 2.234416 | 1.224019 | 0.365926 | -0.197596 | 0.7317 | 0.6987 | 0.4399 | 4.017534 | 3.351575 |
| 2459846 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 2.057513 | 3.908429 | 0.603710 | 5.968341 | 0.086005 | 2.043610 | 0.074741 | -0.484297 | 0.8285 | 0.6796 | 0.5018 | 4.163545 | 4.223727 |
| 2459845 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 2.607374 | 2.827016 | 2.751217 | 10.375021 | 1.977122 | 0.624467 | 1.248808 | 0.844170 | 0.7511 | 0.7533 | 0.3716 | 4.823689 | 6.253309 |
| 2459844 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 45.701652 | 49.382215 | 103.187101 | 125.968376 | 252.626488 | 202.087433 | 44.501072 | 36.570746 | 0.8874 | 0.6019 | 0.6051 | nan | nan |
| 2459843 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 7.472590 | 9.007274 | 16.823468 | 18.652212 | 67.036191 | 74.103397 | -2.567654 | -3.361314 | 0.7544 | 0.7388 | 0.4075 | 0.000000 | 0.000000 |
| 2459842 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 0.852904 | 1.045848 | -0.742385 | -0.597055 | -2.504504 | -2.129935 | -1.522165 | -2.067050 | 0.7641 | 0.6821 | 0.2598 | 4.856349 | 4.844026 |
| 2459841 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 38.718396 | 44.220845 | 109.295873 | 118.023838 | 106.302352 | 105.282291 | 2.402701 | -4.433057 | 0.7559 | 0.7416 | 0.3540 | nan | nan |
| 2459839 | RF_maintenance | 100.00% | - | - | - | - | - | -0.439020 | 0.008366 | 0.279409 | -0.688925 | -0.050008 | 3.114784 | 3.223205 | 12.567113 | nan | nan | nan | nan | nan |
| 2459838 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 5.222949 | 7.143744 | 19.690026 | 22.380823 | 9.674977 | 21.480739 | -2.318338 | -3.035425 | 0.7366 | 0.6970 | 0.4263 | 0.000000 | 0.000000 |
| 2459836 | RF_maintenance | - | 0.00% | 0.00% | 0.00% | - | - | nan | nan | nan | nan | nan | nan | nan | nan | 0.7301 | 0.6799 | 0.3796 | nan | nan |
| 2459835 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 4.910369 | 5.258035 | 11.838137 | 12.292004 | 13.469460 | 6.925469 | -6.740012 | -5.916684 | 0.8056 | 0.6318 | 0.4672 | nan | nan |
| 2459833 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 15.651554 | 16.148823 | 50.862067 | 54.795414 | 113.549025 | 56.822807 | -1.014109 | -8.554260 | 0.8149 | 0.5863 | 0.4921 | nan | nan |
| 2459832 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 11.738787 | 16.646965 | 16.074557 | 18.136689 | 9.220975 | 9.140040 | -1.430201 | -0.098289 | 0.8010 | 0.5052 | 0.5920 | 4.356529 | 3.281162 |
| 2459831 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | -0.471227 | -0.080490 | 0.176660 | -0.641483 | 0.023558 | 4.005368 | 2.277722 | 7.919948 | 0.0287 | 0.0280 | 0.0011 | nan | nan |
| 2459830 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 13.058482 | 17.407883 | 21.438298 | 24.027142 | 25.729120 | 28.413639 | -3.929405 | -5.089067 | 0.7947 | 0.5055 | 0.5776 | 5.141762 | 3.961986 |
| 2459829 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | 0.000000 | 0.000000 |
| 2459828 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 12.181383 | 15.469786 | 16.510869 | 18.387335 | 25.872175 | 27.232688 | -3.322787 | -3.748390 | 0.7883 | 0.5192 | 0.5422 | 7.708441 | 4.235763 |
| 2459827 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 8.962697 | 11.517303 | 26.899563 | 30.472390 | 15.925495 | 20.637384 | -1.626222 | -2.740895 | 0.7575 | 0.6629 | 0.4288 | 8.727166 | 6.042214 |
| 2459826 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 11.884040 | 15.039681 | 22.043883 | 24.834871 | 33.729190 | 36.512764 | -1.650510 | 2.292262 | 0.7856 | 0.5496 | 0.5198 | 0.000000 | 0.000000 |
| 2459825 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 12.603297 | 15.828272 | 16.753776 | 19.001941 | 22.826023 | 24.468412 | 4.272567 | 3.955369 | 0.7806 | 0.5367 | 0.5314 | 0.000000 | 0.000000 |
| 2459824 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 6.071890 | 7.978173 | 22.939239 | 26.854522 | 8.355245 | 16.257871 | 5.144247 | 3.912728 | 0.7044 | 0.7218 | 0.3936 | 0.000000 | 0.000000 |
| 2459823 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 15.723854 | 18.471720 | 18.655558 | 20.926496 | 23.903072 | 28.592853 | 11.841304 | 15.887068 | 0.7403 | 0.6110 | 0.4978 | 0.000000 | 0.000000 |
| 2459822 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 14.478196 | 17.231591 | 20.664758 | 23.083194 | 22.237704 | 24.312224 | -0.611948 | 0.118157 | 0.7775 | 0.5607 | 0.5170 | 0.000000 | 0.000000 |
| 2459821 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 16.201359 | 19.645146 | 20.278630 | 23.023657 | 20.023150 | 22.656788 | -0.632201 | -0.065367 | 0.7592 | 0.5530 | 0.5203 | 4.524731 | 3.762367 |
| 2459820 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 10.919631 | 13.268330 | 24.494212 | 27.606641 | 45.547016 | 55.960817 | -2.906605 | -5.028992 | 0.7597 | 0.6559 | 0.4383 | 0.000000 | 0.000000 |
| 2459817 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 16.138848 | 19.022522 | 16.902310 | 19.075533 | 26.446828 | 30.253538 | 2.083878 | 4.202386 | 0.7641 | 0.5900 | 0.5146 | 0.968686 | 0.957411 |
| 2459816 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 10.858552 | 13.953891 | 24.269030 | 27.400955 | 34.007943 | 38.111135 | -4.567298 | -5.290690 | 0.8296 | 0.5577 | 0.6073 | 4.635153 | 3.352919 |
| 2459815 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | 0.000000 | 0.000000 |
| 2459814 | RF_maintenance | 0.00% | - | - | - | - | - | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 2459813 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | 0.000000 | 0.000000 |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 168 | N15 | RF_maintenance | ee Power | inf | 260.810769 | 259.845248 | inf | inf | 13787.732752 | 13787.704735 | 14631.146631 | 14634.690907 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 168 | N15 | RF_maintenance | nn Shape | 2.094663 | 1.478382 | 2.094663 | 0.205210 | -0.225372 | 0.501435 | 0.268022 | -0.094797 | 0.041398 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 168 | N15 | RF_maintenance | nn Shape | 2.060712 | 1.767493 | 2.060712 | 0.004101 | 1.662762 | 0.563941 | 0.305327 | 0.643569 | -0.146320 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 168 | N15 | RF_maintenance | nn Power | 6.952587 | 2.517948 | 2.061295 | 6.952587 | 1.328715 | 0.324796 | 1.482289 | -0.304011 | 0.477724 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 168 | N15 | RF_maintenance | nn Power | 6.296933 | 2.459872 | 1.989204 | 6.296933 | 0.923457 | 1.224019 | 2.234416 | -0.197596 | 0.365926 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 168 | N15 | RF_maintenance | nn Power | 5.968341 | 2.057513 | 3.908429 | 0.603710 | 5.968341 | 0.086005 | 2.043610 | 0.074741 | -0.484297 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 168 | N15 | RF_maintenance | nn Power | 10.375021 | 2.827016 | 2.607374 | 10.375021 | 2.751217 | 0.624467 | 1.977122 | 0.844170 | 1.248808 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 168 | N15 | RF_maintenance | ee Temporal Variability | 252.626488 | 45.701652 | 49.382215 | 103.187101 | 125.968376 | 252.626488 | 202.087433 | 44.501072 | 36.570746 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 168 | N15 | RF_maintenance | nn Temporal Variability | 74.103397 | 9.007274 | 7.472590 | 18.652212 | 16.823468 | 74.103397 | 67.036191 | -3.361314 | -2.567654 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 168 | N15 | RF_maintenance | nn Shape | 1.045848 | 0.852904 | 1.045848 | -0.742385 | -0.597055 | -2.504504 | -2.129935 | -1.522165 | -2.067050 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 168 | N15 | RF_maintenance | nn Power | 118.023838 | 38.718396 | 44.220845 | 109.295873 | 118.023838 | 106.302352 | 105.282291 | 2.402701 | -4.433057 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 168 | N15 | RF_maintenance | nn Temporal Discontinuties | 12.567113 | 0.008366 | -0.439020 | -0.688925 | 0.279409 | 3.114784 | -0.050008 | 12.567113 | 3.223205 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 168 | N15 | RF_maintenance | nn Power | 22.380823 | 7.143744 | 5.222949 | 22.380823 | 19.690026 | 21.480739 | 9.674977 | -3.035425 | -2.318338 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 168 | N15 | RF_maintenance | ee Temporal Variability | 13.469460 | 5.258035 | 4.910369 | 12.292004 | 11.838137 | 6.925469 | 13.469460 | -5.916684 | -6.740012 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 168 | N15 | RF_maintenance | ee Temporal Variability | 113.549025 | 16.148823 | 15.651554 | 54.795414 | 50.862067 | 56.822807 | 113.549025 | -8.554260 | -1.014109 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 168 | N15 | RF_maintenance | nn Power | 18.136689 | 11.738787 | 16.646965 | 16.074557 | 18.136689 | 9.220975 | 9.140040 | -1.430201 | -0.098289 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 168 | N15 | RF_maintenance | nn Temporal Discontinuties | 7.919948 | -0.471227 | -0.080490 | 0.176660 | -0.641483 | 0.023558 | 4.005368 | 2.277722 | 7.919948 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 168 | N15 | RF_maintenance | nn Temporal Variability | 28.413639 | 13.058482 | 17.407883 | 21.438298 | 24.027142 | 25.729120 | 28.413639 | -3.929405 | -5.089067 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 168 | N15 | RF_maintenance | nn Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 168 | N15 | RF_maintenance | nn Temporal Variability | 27.232688 | 15.469786 | 12.181383 | 18.387335 | 16.510869 | 27.232688 | 25.872175 | -3.748390 | -3.322787 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 168 | N15 | RF_maintenance | nn Power | 30.472390 | 8.962697 | 11.517303 | 26.899563 | 30.472390 | 15.925495 | 20.637384 | -1.626222 | -2.740895 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 168 | N15 | RF_maintenance | nn Temporal Variability | 36.512764 | 15.039681 | 11.884040 | 24.834871 | 22.043883 | 36.512764 | 33.729190 | 2.292262 | -1.650510 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 168 | N15 | RF_maintenance | nn Temporal Variability | 24.468412 | 15.828272 | 12.603297 | 19.001941 | 16.753776 | 24.468412 | 22.826023 | 3.955369 | 4.272567 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 168 | N15 | RF_maintenance | nn Power | 26.854522 | 6.071890 | 7.978173 | 22.939239 | 26.854522 | 8.355245 | 16.257871 | 5.144247 | 3.912728 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 168 | N15 | RF_maintenance | nn Temporal Variability | 28.592853 | 18.471720 | 15.723854 | 20.926496 | 18.655558 | 28.592853 | 23.903072 | 15.887068 | 11.841304 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 168 | N15 | RF_maintenance | nn Temporal Variability | 24.312224 | 14.478196 | 17.231591 | 20.664758 | 23.083194 | 22.237704 | 24.312224 | -0.611948 | 0.118157 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 168 | N15 | RF_maintenance | nn Power | 23.023657 | 19.645146 | 16.201359 | 23.023657 | 20.278630 | 22.656788 | 20.023150 | -0.065367 | -0.632201 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 168 | N15 | RF_maintenance | nn Temporal Variability | 55.960817 | 10.919631 | 13.268330 | 24.494212 | 27.606641 | 45.547016 | 55.960817 | -2.906605 | -5.028992 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 168 | N15 | RF_maintenance | nn Temporal Variability | 30.253538 | 16.138848 | 19.022522 | 16.902310 | 19.075533 | 26.446828 | 30.253538 | 2.083878 | 4.202386 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 168 | N15 | RF_maintenance | nn Temporal Variability | 38.111135 | 13.953891 | 10.858552 | 27.400955 | 24.269030 | 38.111135 | 34.007943 | -5.290690 | -4.567298 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 168 | N15 | RF_maintenance | nn Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 168 | N15 | RF_maintenance | nn Shape | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 168 | N15 | RF_maintenance | nn Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |